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Automate tasks with headless mode

Automate tasks with Gemini CLI. Learn how to use headless mode, pipe data into Gemini CLI, automate workflows with shell scripts, and generate structured JSON output for other applications.

Prerequisites

  • Gemini CLI installed and authenticated.
  • Familiarity with shell scripting (Bash/Zsh).

Why headless mode?

Headless mode runs Gemini CLI once and exits. It's perfect for:

  • CI/CD: Analyzing pull requests automatically.
  • Batch processing: Summarizing a large number of log files.
  • Tool building: Creating your own "AI wrapper" scripts.

How to use headless mode

Run Gemini CLI in headless mode by providing a prompt as a positional argument. This bypasses the interactive chat interface and prints the response to standard output (stdout).

Run a single command:

gemini "Write a poem about TypeScript"

How to pipe input to Gemini CLI

Feed data into Gemini using the standard Unix pipe |. Gemini reads the standard input (stdin) as context and answers your question using standard output.

Pipe a file:

cat error.log | gemini "Explain why this failed"

Pipe a command:

git diff | gemini "Write a commit message for these changes"

Use Gemini CLI output in scripts

Because Gemini prints to stdout, you can chain it with other tools or save the results to a file.

Scenario: Bulk documentation generator

You have a folder of Python scripts and want to generate a README.md for each one.

  1. Save the following code as generate_docs.sh:

    #!/bin/bash

# Loop through all Python files
for file in *.py; do
  echo "Generating docs for $file..."

  # Ask Gemini CLI to generate the documentation and print it to stdout
  gemini "Generate a Markdown documentation summary for @$file. Print the
  result to standard output." > "${file%.py}.md"
done

  2. Make the script executable and run it in your directory:

    chmod +x generate_docs.sh
./generate_docs.sh

    This creates a corresponding Markdown file for every Python file in the folder.

Extract structured JSON data

When writing a script, you often need structured data (JSON) to pass to tools like jq. To get pure JSON data from the model, combine the --output-format json flag with jq to parse the response field.

Scenario: Extract and return structured data

  1. Save the following script as generate_json.sh:

    #!/bin/bash

# Ensure we are in a project root
if [ ! -f "package.json" ]; then
  echo "Error: package.json not found."
  exit 1
fi

# Extract data
gemini --output-format json "Return a raw JSON object with keys 'version' and 'deps' from @package.json" | jq -r '.response' > data.json

  2. Run generate_json.sh:

    chmod +x generate_json.sh
./generate_json.sh

  3. Check data.json. The file should look like this:

    {
  "version": "1.0.0",
  "deps": {
    "react": "^18.2.0"
  }
}

Build your own custom AI tools

Use headless mode to perform custom, automated AI tasks.

Scenario: Create a "Smart Commit" alias

You can add a function to your shell configuration (like .zshrc or .bashrc) to create a git commit wrapper that writes the message for you.

  1. Open your .zshrc file (or .bashrc if you use Bash) in your preferred text editor.

    nano ~/.zshrc

    Note: If you use VS Code, you can run code ~/.zshrc.

  2. Scroll to the very bottom of the file and paste this code:

    function gcommit() {
  # Get the diff of staged changes
  diff=$(git diff --staged)

  if [ -z "$diff" ]; then
    echo "No staged changes to commit."
    return 1
  fi

  # Ask Gemini to write the message
  echo "Generating commit message..."
  msg=$(echo "$diff" | gemini "Write a concise Conventional Commit message for this diff. Output ONLY the message.")

  # Commit with the generated message
  git commit -m "$msg"
}

    Save your file and exit.

  3. Run this command to make the function available immediately:

    source ~/.zshrc

  4. Use your new command:

    gcommit

    Gemini CLI will analyze your staged changes and commit them with a generated message.

Next steps